Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model.Although the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation parameter.In this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter estimation.The proposed model highlights the residual region with considerable information and constructs color saliency.Second,we incorporate the content-based color saliency as spatial information in the Gaussian mixture model.The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria.Finally,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic objectives.For experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset.In the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

Spatial weight matrix in dimensionality reduction reconstruction for microelectromechanical system-based photoacoustic microscopy

A micro-electromechanical system(MEMS)scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy(OR-PAM).However,the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image.Moreover,the size of the airy disk,ultrasonic sensor properties,and thermal effects decrease the resolution.Thus,in this study,we proposed a spatial weight matrix(SWM)with a dimensionality reduction for image reconstruction.The three-layer SWM contains the invariable information of the system,which includes a spatial dependent distortion correction and 3D deconvolution.We employed an ordinal-valued Markov random field and the Harris Stephen algorithm,as well as a modified delay-and-sum method during a time reversal.The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM;this is also true for severely distorted images.The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index,on average.Moreover,the peak signal-to-noise ratio was increased by 17.08%after 3D deconvolution.This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

Spatial search weighting information contained in cell velocity distribution

Cell migration plays a significant role in physiological and pathological processes.Understanding the characteristics of cell movement is crucial for comprehending biological processes such as cell functionality,cell migration,and cell–cell interactions.One of the fundamental characteristics of cell movement is the specific distribution of cell speed,containing valuable information that still requires comprehensive understanding.This article investigates the distribution of mean velocities along cell trajectories,with a focus on optimizing the efficiency of cell food search in the context of the entire colony.We confirm that the specific velocity distribution in the experiments corresponds to an optimal search efficiency when spatial weighting is considered.The simulation results indicate that the distribution of average velocity does not align with the optimal search efficiency when employing average spatial weighting.However,when considering the distribution of central spatial weighting,the specific velocity distribution in the experiment is shown to correspond to the optimal search efficiency.Our simulations reveal that for any given distribution of average velocity,a specific central spatial weighting can be identified among the possible central spatial weighting that aligns with the optimal search strategy.Additionally,our work presents a method for determining the spatial weights embedded in the velocity distribution of cell movement.Our results have provided new avenues for further investigation of significant topics,such as relationship between cell behavior and environmental conditions throughout their evolutionary history,and how cells achieve collective cooperation through cell-cell communication.

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains, China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R^2=0.55 vs.R^2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.

Inversion of gravity gradient data based on spatial gradient weighting

Compared with traditional gravity measurement data,gravity gradient tensor data contain more high frequency information,which can be used to understand the earth's interior structure,mineral resources distribution etc. In this study,the authors present an algorithm for inverting gravity gradiometer data to recover the three-dimensional( 3-D) distributions of density. Spatial gradient weighting was used to constrain the extent of the body horizontally and vertically. A more accurate inversion result can be obtained by combining the prior information into the weighting function and applying it in inversion. This method was tested on synthetic models and the inverted results showed that the resolution was significantly improved. Moreover,the algorithm was applied to the inversion of empirical data from a salt dome located in Texas,USA,which demonstrated the validity of the proposed method.

Spatial Downscaling of the Tropical Rainfall Measuring Mission Precipitation Using Geographically Weighted Regression Kriging over the Lancang River Basin, China

Satellite-based precipitation products have been widely used to estimate precipitation, especially over regions with sparse rain gauge networks. However, the low spatial resolution of these products has limited their application in localized regions and watersheds.This study investigated a spatial downscaling approach, Geographically Weighted Regression Kriging(GWRK), to downscale the Tropical Rainfall Measuring Mission(TRMM) 3 B43 Version 7 over the Lancang River Basin(LRB) for 2001–2015. Downscaling was performed based on the relationships between the TRMM precipitation and the Normalized Difference Vegetation Index(NDVI), the Land Surface Temperature(LST), and the Digital Elevation Model(DEM). Geographical ratio analysis(GRA) was used to calibrate the annual downscaled precipitation data, and the monthly fractions derived from the original TRMM data were used to disaggregate annual downscaled and calibrated precipitation to monthly precipitation at 1 km resolution. The final downscaled precipitation datasets were validated against station-based observed precipitation in 2001–2015. Results showed that: 1) The TRMM 3 B43 precipitation was highly accurate with slight overestimation at the basin scale(i.e., CC(correlation coefficient) = 0.91, Bias = 13.3%). Spatially, the accuracies of the upstream and downstream regions were higher than that of the midstream region. 2) The annual downscaled TRMM precipitation data at 1 km spatial resolution obtained by GWRK effectively captured the high spatial variability of precipitation over the LRB. 3) The annual downscaled TRMM precipitation with GRA calibration gave better accuracy compared with the original TRMM dataset. 4) The final downscaled and calibrated precipitation had significantly improved spatial resolution, and agreed well with data from the validated rain gauge stations, i.e., CC = 0.75, RMSE(root mean square error) = 182 mm, MAE(mean absolute error) = 142 mm, and Bias = 0.78%for annual precipitation and CC = 0.95, RMSE = 25 mm, MAE = 16 mm, and Bias = 0.67% for monthly precipitation.

Comparison of Uniform and Kernel Gaussian Weight Matrix in Generalized Spatial Panel Data Model

Panel data combine cross-section data and time series data. If the cross-section is locations, there is a need to check the correlation among locations. ρ and λ are parameters in generalized spatial model to cover effect of correlation between locations. Value of ρ or λ will influence the goodness of fit model, so it is important to make parameter estimation. The effect of another location is covered by making contiguity matrix until it gets spatial weighted matrix (W). There are some types of W—uniform W, binary W, kernel Gaussian W and some W from real case of economics condition or transportation condition from locations. This study is aimed to compare uniform W and kernel Gaussian W in spatial panel data model using RMSE value. The result of analysis showed that uniform weight had RMSE value less than kernel Gaussian model. Uniform W had stabil value for all the combinations.

湖北省城市洪涝灾害韧性时空演变及驱动因素分析

洪涝灾害是湖北省主要自然灾害之一。开展湖北省城市洪涝灾害韧性研究,可加快该地区韧性城市建设,促进城市的可持续健康发展。以湖北省17个城市为研究对象,选取2010-2021年为研究期,从压力、状态、响应等维度构建城市洪涝灾害韧性评价指标体系,运用CRITIC-熵权组合赋权法分析其洪涝灾害韧性时空演变,并利用空间自相关法分析其洪涝灾害韧性空间集聚特征,再用地理探测器模型分析其驱动因素。结果表明:①研究期内湖北省城市洪涝灾害韧性指数呈波浪式上升趋势,增幅达34.82%;②城市洪涝灾害韧性的空间分布主要呈现为“西高东低”和以武汉为高值向外递减,城市洪涝灾害韧性的空间集聚效应较强;③城市洪涝灾害韧性的空间分异性为多因素共同作用。其中,人口暴露程度、地形起伏情况、地表陡缓程度、医疗保障能力为主要驱动因素。研究结果可为城市防洪减灾政策提供参考。

“双碳”背景下主产区粮食生产减污降碳综合效益评价

基于2002—2021年中国13个粮食主产区的面板数据,采用熵权TOPSIS法、障碍因子诊断模型与Dagum基尼系数,实证分析主产区粮食生产减污降碳综合效益及其时空演化。结果表明:2002—2021年粮食主产区综合效益指数由0.388提高至0.878,整体为上升态势;主产区综合效益水平呈现“东高西低”的空间分布特征;核心障碍因素存在由粮食产出效率与粮食产出水平向粮食生产投入集约化水平与粮食生产碳排放强度集中的转变过程;总体差异水平未出现进一步缓解的趋势,空间差异主要来源于区域间差异,样本期贡献率均值为56.89%,东西部间差异最大,东部、中部地区区域内的基尼系数水平也存在上升趋势,说明缓解区域内差异同样不容忽视。据此,建议切实提高资源投入集约化程度,统筹协调各区域综合效益均衡发展,建立绿色粮食生产体系,强化绿色低碳收益,进而实现农业高质量发展。

Spatial spillover effects of transport infrastructure on regional economic growth

Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to 2003,a spatial production function is applied to examine the spatial spillovers which can be generated as a positive output spillover from the transport infrastructure between neighboring cities.Some spatial weighted matrices are adopted to define different neighboring cities to measure how easily factors or economic activities can migrate between regions.The estimation results show that the output elasticity of the highway infrastructure in 11 cities are all insignificant at a 5% significance level;hence,highway infrastructure in a region cannot explain the same region's economic growth.On the other hand,the highway infrastructure of other contiguous regions has positive spillover effects on a same region's economic growth.

结构瞬变影响下采场煤岩静态力学响应的阶变规律

为了明确采场煤岩力学响应的演变规律,以宽沟煤矿W1143长壁综采工作面为工程背景,采用物理相似模拟实验和数值计算相结合的方法,在明确来压前、后覆岩空间结构瞬变特征的基础之上,建立了一种实现坚硬岩层瞬时破断的数值模拟前处理方法,研究了结构瞬变影响下采场煤岩静态力学响应的阶变规律。结果表明:覆岩空间结构是由破裂面以外、采动影响范围以内不同岩性的岩层所组成的空间岩体结构,是其影响范围内的采场煤岩产生不同力学响应的内因。来压期间,由于坚硬岩层的破断具有瞬时性,导致覆岩空间结构发生瞬变,受此影响,采场煤岩的静态力学响应发生阶变,其本质上为一动力学过程。覆岩空间结构瞬变造成采场煤岩的应力场与位移场产生阶变,阶变集中于瞬变区域附近,距离瞬变区域越远,其阶变量越小。

可达性对城市群多模式交通碳排放的空间异质性影响

当前,在交通行业碳减排存在巨大挑战以及未来中国城市群交通发展长远规划双重背景下,如何通过改善交通可达性提高居民出行效率和减少碳排放是亟待解决的关键问题之一。本文基于城际出行手机信令数据,从居民出行的角度提出城际多模式交通客运碳排放量方法,并采用梯度提升决策树(GBDT)模型及多尺度地理加权回归(MGWR)模型探讨可达性对区域碳排放量的空间异质性影响。以关中平原城市群为例进行验证,结果表明:城际公路客运碳排放量远大于铁路,呈现沿交通基础设施线路分布的特征;在整体区域范围内,可达性指标对碳排放水平具有一定的正向边际效应;MGWR能够刻画碳排放与可达性指标关系的空间异质性及尺度差异;经济潜能可达性、介数中心性及接近中心性对城际碳排放具有显著的正向空间异质性影响,但影响尺度不同;公路客运碳排放对介数中心性及接近中心性要素较为敏感,经济潜能对碳排放的影响较为平稳;铁路出行可达性的提升对中心城市的影响效应低于周边区县城市。

基于GIS的泸西县农业用地适宜性评价分析

通过GIS的空间分析以及主成份分析,对泸西县农业用地适宜性进行评价与分析。确定了该县农用地适宜性评价的参评因子及其影响度(隶属度),建立了适宜性评价指标与分类体系,划分农业用地适宜性等级,编绘农业适宜性等级图,为科学规划、合理利用、有效保护土地资源,促进可持续发展提供依据。

一类变系数空间滞后的混合地理加权回归模型

为解决因变量空间滞后存在的局域性问题,对现有常系数空间滞后的混合地理加权回归模型作了具有更广泛形式的拓展,提出一类变系数空间滞后的混合地理加权回归(MGWR-VSLR)模型。MGWR-VSLR模型实现了空间相关性与空间异质性融合,涵盖了绝大多数地理加权回归的模型形式,基于重构参数化方法和似然比检验分别给出模型的系数估计方法与显著性检验以及选取变系数的判别检验。在蒙特卡罗模拟与实际应用中,MGWR-VSLR模型均表现出优异的因变量拟合与预测能力。MGWR-VSLR模型的提出为定量化研究空间效应问题设定适宜的模型形式提供了支撑依据。

湖南省城镇土地利用多样性对人口密度的影响研究

城镇人口密度降低已经成为制约部分城镇发展的重要因素之一。选取湖南省为案例区,在定量刻画湖南省城镇人口密度与土地利用多样性格局基础上,运用地理加权回归模型探索两者的关系,并构建压力-状态-响应模型探索湖南省城镇土地利用多样性对人口密度的影响路径。结果表明:①湖南省城镇人口密度、城镇建设用地土地利用多样性均存在明显的空间正相关性,但聚集趋势相反;②单一化的城镇建设用地利用方式既不利于湖南省城镇土地利用多样性的提升,也不利于湖南省城镇人口密度的提升,工业布局可以引导湖南省城镇人口密度的提升,注重经济发展、提高城镇公共服务能力是提高城镇土地利用多样性进而提高城镇人口密度的有效方法;③湖南省二、三产业增加值与城镇人口密度呈现弱负相关且呈现“南高北低”的格局;④湖南省城镇居民可支配收入与城镇人口密度的相关性呈现出“西北高、东南低”的格局,并在长沙市中心城区一带出现正相关与负相关的分界。最后,从优化城镇职能体系对应对历史遗留问题、促进人口密度有序提升方面提出了政策建议。

中国省域旅游环境系统服务价值的时空分异及影响因素研究

探究旅游环境系统服务价值的时空分异及影响因素对于科学评估旅游环境系统服务价值,实现旅游业的可持续发展具有重要意义。文章界定旅游环境系统服务价值的内涵,以我国31个省域为研究对象,从经济、生态和社会三个层面构建省域旅游环境系统服务价值指标体系,采用熵权TOPSIS法和空间自相关法对31个省域的旅游环境系统服务价值进行测度和时空分异特征探究,通过地理探测器和地理加权回归模型分析其影响因素。结果表明:(1)2012—2019年,31个省域旅游环境系统服务价值均值在时间层面总体呈上升趋势,2020年因新冠疫情出现轻微下降,2021年有所回升;空间层面上,东、中、西三大区域空间格局差异显著,年均值东部>中部>西部。(2)31个省域旅游环境系统服务价值的全局莫兰指数呈现下降—上升—下降—上升—平稳的小幅波动趋势,存在空间正向集聚效应,且局部自相关集聚模式变化较大。(3)对外经济贸易水平、政府扶持力度和科技投入对旅游环境系统服务价值均为正向影响,高等教育规模呈负向影响,对外经济贸易水平的影响强度由北向南递增,政府扶持力度和高等教育规模的影响强度由西北向东南递增,科技投入的影响强度由西向东递减。

基于改进YOLOv5s的跌倒行为检测

为了实现电厂人员跌倒行为的实时检测,防止跌倒昏迷而无法被及时发现并救援的事件发生,针对跌倒行为检测实时性以及特征提取能力不足的问题,提出了一种改进YOLOv5s的跌倒行为检测算法网络:在YOLOv5s模型中引入SKAttention注意力模块,使得网络可以自动地利用对分类有效的感受野捕捉到的信息,这种新的深层结构允许CNN在卷积核心上执行动态选择机制,从而自适应地调整其感受野的大小;同时结合ASFF自适应空间融合,并在其中充分利用不同特征,又在算法中引入权重参数,以多层次功能为基础,实现了水下目标识别精度提升的目标;加入空间金字塔池化结构SPPFCSPC,大幅缩短了推理时间;实验结果表明,相比于原始YOLOv5s,新网络在mAP平均精度均值方面提升了2.1%,查全率提升了16%;改进后的网络在感知细节和空间建模方面更加强大,能够更准确地捕捉到人员跌倒的异常行为,检测效果有了显著提升。

基于时变空间权重矩阵面板数据模型的京津冀产业转移环境效应研究

环境污染尤其是大气污染成为影响京津冀地区社会发展一个不容忽视的问题,环境库兹涅茨曲线(EKC)是分析环境问题的常用工具,而很多相关文献的模型未考虑到空间溢出效应及其时间传导途径,忽视了产业转移对环境污染的影响效应。基于以上问题,本文在分析中引入产业转移指数,以空间面板数据模型为分析工具,采用一种内生时空权重矩阵对京津冀地区开展分析。研究结果表明:产业转移对邻近地区环境影响作用更显著,三产比重的提高能有效缓解当地环境污染,京津冀地区工业二氧化硫排放与GDP增长呈正N型。因此,提高高污染工业转移门槛和工业排污标准、提高三产占比以及提高工业固体废物综合利用率对京津冀地区以及其邻近地区环境改善有重要意义。

数字经济空间关联与区域经济增长——来自中国省域层面的经验证据

文章通过构建有向加权的空间关联网络刻画省域间数字经济空间关联关系,实证检验网络节点特征对区域经济增长的影响。研究发现:2013—2021年,我国数字经济整体发展水平显著提升,但区域间发展差异突出。我国省际数字经济空间关联网络的通达性不强,呈现地理聚集性,具有典型的“核心-边缘”特征,由东部发达地区构成的净受益板块对数字资源的“虹吸效应”明显。此外,网络中节点地位的差异性明显,具有明显的层级分化特征,节点地区有向转变中心性和控制力对区域经济增长有显著的正向影响,说明数字经济产生的区域联动效应和关联溢出效应对提升经济发展水平具有重要的驱动作用。

经济高质量发展的空间差异性研究

经济高质量发展对经济的可持续发展产生正能量,也可以推动整个经济体系迈进现代化之路。以中国30个省份作为研究对象,通过熵权TOPSIS法测算中国30个省份2010—2021年经济高质量发展现状,测算结果显示:近年来中国各省份经济高质量发展整体呈现上升趋势。在此基础上通过莫兰指数分别分析各省份经济高质量发展在整体和局部存在的空间差异,分析结果显示:中国各省份经济高质量整体莫兰指数呈现显著的空间集聚效应;在分析局部集聚程度时,不同地区在不同年份呈现出不同的集聚程度。